The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Yoshifumi Watanabe - One of the best experts on this subject based on the ideXlab platform.
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A qualitative and quantitative analysis of rhythmic activities during a Mental Task and sleep spindles.
Progress in neuro-psychopharmacology & biological psychiatry, 2002Co-Authors: Masatomo Suetsugi, Yasushi Mizuki, Itsuko Ushijima, Yoshifumi WatanabeAbstract:The frequency, configuration, and distribution of sleep spindles are similar to some of the rhythmic activities seen during Task performance. In the present study, the relationship between rhythmic activities during sleep and arithmetic addition was investigated in male university students with (n = 10) and without (n = 10) frontal midline theta activity (Fmtheta). Electroencephalograms (EEGs) during addition in both groups were compared at frontal and central areas on three consecutive days. Polysomnograms were recorded at the same regions on four consecutive nights for each group. The amount of theta rhythm during a Mental Task (Fmtheta) and in nocturnal sleep at Fz and Cz electrodes was greater for the Fmtheta group than for the non-Fmtheta group, while the amount of beta rhythm at both sites was smaller in the Fmtheta group than in the non-Fmtheta group. There were no differences between the groups in the amount of alpha rhythm at either site. The frequency of alpha rhythm at Fz and Cz in both situations was slower for the Fmtheta group than for the non-Fmtheta group, but there were no differences in the frequency of theta and beta rhythms between the groups at either site. These results suggest that rhythmic activities during a Mental Task and in sleep may correlate with each other.
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The relationship between rhythmic activities during a Mental Task and sleep spindles: a correlative analysis.
Progress in neuro-psychopharmacology & biological psychiatry, 2002Co-Authors: Masatomo Suetsugi, Yasushi Mizuki, Itsuko Ushijima, Yoshifumi WatanabeAbstract:In a previous study, we suggested that the characteristics of theta, alpha, and beta rhythms during a Mental Task were similar to those during sleep. Building upon the previous data, correlations between rhythmic activities during a Mental Task and during sleep were investigated in the present study. Patterns of correlation and no correlation between rhythmic activities during the Mental Task were similar to those during sleep for subjects with and without frontal midline theta (Fmtheta) activity. In the Fmtheta group, there were no correlations between rhythmic activities in the two situations, while in the non-Fmtheta subjects, theta and alpha rhythms showed a positive correlation with one another, and theta and beta rhythms correlated negatively during sleep. In both groups, there were many correlations between rhythmic activities during the Mental Task and those in Sleep Stage 2, while there were few correlations between rhythmic activities during the Mental Task and those in other sleep stages. These results suggest that the mechanism generating rhythmic activities during the appearance of rhythmic activities induced by a Mental Task may be closely related to those of rhythmic activities during sleep, and that the membrane potentials in reticular thalamic (RE) neurons during the appearance of rhythmic activities induced by a Mental Task may be nearly equivalent to that in Sleep Stage 2, and that the correlation pattern between the rhythmic activities in each group may be well explained by the appearance pattern of each rhythm in the previous report.
Masatomo Suetsugi - One of the best experts on this subject based on the ideXlab platform.
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A qualitative and quantitative analysis of rhythmic activities during a Mental Task and sleep spindles.
Progress in neuro-psychopharmacology & biological psychiatry, 2002Co-Authors: Masatomo Suetsugi, Yasushi Mizuki, Itsuko Ushijima, Yoshifumi WatanabeAbstract:The frequency, configuration, and distribution of sleep spindles are similar to some of the rhythmic activities seen during Task performance. In the present study, the relationship between rhythmic activities during sleep and arithmetic addition was investigated in male university students with (n = 10) and without (n = 10) frontal midline theta activity (Fmtheta). Electroencephalograms (EEGs) during addition in both groups were compared at frontal and central areas on three consecutive days. Polysomnograms were recorded at the same regions on four consecutive nights for each group. The amount of theta rhythm during a Mental Task (Fmtheta) and in nocturnal sleep at Fz and Cz electrodes was greater for the Fmtheta group than for the non-Fmtheta group, while the amount of beta rhythm at both sites was smaller in the Fmtheta group than in the non-Fmtheta group. There were no differences between the groups in the amount of alpha rhythm at either site. The frequency of alpha rhythm at Fz and Cz in both situations was slower for the Fmtheta group than for the non-Fmtheta group, but there were no differences in the frequency of theta and beta rhythms between the groups at either site. These results suggest that rhythmic activities during a Mental Task and in sleep may correlate with each other.
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The relationship between rhythmic activities during a Mental Task and sleep spindles: a correlative analysis.
Progress in neuro-psychopharmacology & biological psychiatry, 2002Co-Authors: Masatomo Suetsugi, Yasushi Mizuki, Itsuko Ushijima, Yoshifumi WatanabeAbstract:In a previous study, we suggested that the characteristics of theta, alpha, and beta rhythms during a Mental Task were similar to those during sleep. Building upon the previous data, correlations between rhythmic activities during a Mental Task and during sleep were investigated in the present study. Patterns of correlation and no correlation between rhythmic activities during the Mental Task were similar to those during sleep for subjects with and without frontal midline theta (Fmtheta) activity. In the Fmtheta group, there were no correlations between rhythmic activities in the two situations, while in the non-Fmtheta subjects, theta and alpha rhythms showed a positive correlation with one another, and theta and beta rhythms correlated negatively during sleep. In both groups, there were many correlations between rhythmic activities during the Mental Task and those in Sleep Stage 2, while there were few correlations between rhythmic activities during the Mental Task and those in other sleep stages. These results suggest that the mechanism generating rhythmic activities during the appearance of rhythmic activities induced by a Mental Task may be closely related to those of rhythmic activities during sleep, and that the membrane potentials in reticular thalamic (RE) neurons during the appearance of rhythmic activities induced by a Mental Task may be nearly equivalent to that in Sleep Stage 2, and that the correlation pattern between the rhythmic activities in each group may be well explained by the appearance pattern of each rhythm in the previous report.
Tetsuo Kirimoto - One of the best experts on this subject based on the ideXlab platform.
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development of a Mental disorder screening system using support vector machine for classification of heart rate variability measured from single lead electrocardiography
Static Analysis Symposium, 2019Co-Authors: Mai Kobayashi, Toshikazu Shinba, Takemi Matsui, Tetsuo KirimotoAbstract:The diagnosis of psychiatric disorders, such as major depressive disorder (MDD), depends on clinical interviews and assessment of symptoms. However, due to the fact that Mental states cannot be objectively assessed, diagnosis procedures are often influenced by clinical experience of psychiatrist. Hence, the aim of this study is to develop a simple, objective, highly accurate self-check system for screening of psychiatric disorders based on heart rate variability (HRV) measured from single- lead electrocardiography (ECG) or photoplethysmogram (PPG) for home healthcare monitoring. HRV is widely used as objective biomarker for assessment of autonomic nerve system. The low frequency (LF) of HRV originates from the sympathetic and parasympathetic nerves. The high frequency (HF) originates from the parasympathetic nerves. In our previous clinical trial, we confirmed that HRV of MDD patients is less reactive than healthy subjects during a Mental Task (generate random numbers) condition. However, Mental Task alone is difficult to assess HRV accurately owing to influence of measurement condition and individual differences. In this study, we implemented a single-lead ECG system based on reactivity of HRV, and combined Mental Task and paced deep breathing thereby improving the screening accuracy. Moreover, support vector machine (SVM) model was applied for classification of HRV indices. We tested the system on 16 healthy subjects and 7 psychiatric patients with depression or somatoform disorder. A significant difference was found between the healthy group and the patient group for the response of the HRV indices on dual Mental Tasks. The SVM non-linear classification model achieved a sensitivity of 71.4% and specificity of 93.8%.
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simple and objective screening of major depressive disorder by heart rate variability analysis during paced respiration and Mental Task conditions
International Conference of the IEEE Engineering in Medicine and Biology Society, 2017Co-Authors: Mai Kobayashi, Guanghao Sun, Toshikazu Shinba, Takemi Matsui, Tetsuo KirimotoAbstract:Since objective biomarkers for major depressive disorder (MDD) are not readily available, clinical psychiatrists diagnose patients with MDD subjectively based on clinical interviews and diagnostic criteria. It often raises various concerns, including false responses by patients, subjective factors, and inexperience of the attendants leading to incorrect diagnosis. Here, we developed a self-monitoring system for simple and objective screening of MDD using a photoplethysmography (PPG) sensor and a 24-GHz microwave radar, which was based on the analysis of heart rate variability (HRV) during paced respiration and Mental Task conditions. In our previous study, we assessed the reactivity of HRV measurements during a Mental Task (random number generation) condition in patients with MDD and healthy control subjects. The HRV indices are less reactive in patients with MDD compared to healthy subjects during the Mental Task, which enabled us to identify the patients at risk for depression. In this study, the reactivity of HRV was measured not only in the Mental Task but also during paced respiration (i.e., 5-s inhalation and 5-s exhalation) conditions, thereby assessing more detailed autonomic nervous system (ANS) activity via HRV indices. To investigate the effect of paced respiration on MDD screening, we compared the ANS activity via HRV indices in with/without paced respiration conditions in 28 drug-naive patients with MDD and 27 healthy control subjects. The result showed that ANS significantly activated during the paced respiration condition (p<0.05). The sensitivity in detecting patients with MDD was 86% under paced respiration and Mental Task conditions, which was higher than the sensitivity (68%) under Mental Task condition alone.
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an objective screening method for major depressive disorder using logistic regression analysis of heart rate variability data obtained in a Mental Task paradigm
Frontiers in Psychiatry, 2016Co-Authors: Guanghao Sun, Toshikazu Shinba, Tetsuo Kirimoto, Takemi MatsuiAbstract:Background and Objectives: Heart rate variability (HRV) has been intensively studied as a promising biological marker of major depressive disorder (MDD). Our previous study confirmed that autonomic activity and reactivity in depression revealed by HRV during rest and Mental Task (MT) conditions can be used as diagnostic measures and in clinical evaluation. In this study, logistic regression analysis (LRA) was utilized for the classification and prediction of MDD based on HRV data obtained in an MT paradigm. Methods: Power spectral analysis of HRV on R-R intervals before, during, and after an MT (random number generation) was performed in 44 drug-naive patients with MDD and 47 healthy control subjects at Department of Psychiatry in Shizuoka Saiseikai General Hospital. Logit scores of LRA determined by HRV indices and heart rates discriminated patients with MDD from healthy subjects. The high frequency (HF) component of HRV and the ratio of the low frequency (LF) component to the HF component (LF/HF) correspond to parasympathetic and sympathovagal balance, respectively. Results: The LRA achieved a sensitivity and specificity of 80.0% and 79.0%, respectively, at an optimum cutoff logit score (0.28). Misclassifications occurred only when the logit score was close to the cutoff score. Logit scores also correlated significantly with subjective self-rating depression scale scores (p < 0.05). Conclusion: HRV indices recorded during a Mental Task may be an objective tool for screening patients with MDD in psychiatric practice. The proposed method appears promising for not only objective and rapid MDD screening, but also evaluation of its severity.
Mukesh Prasad - One of the best experts on this subject based on the ideXlab platform.
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A hierarchical meta-model for multi-class Mental Task based brain-computer interfaces
Neurocomputing, 2020Co-Authors: Akshansh Gupta, Rahul Agrawal, Jyoti Singh Kirar, Baljeet Kaur, Weiping Ding, Chin-teng Lin, Javier Andreu-perez, Mukesh PrasadAbstract:Abstract In the last few years, many research works have been suggested on Brain-Computer Interface (BCI), which assists severely physically disabled persons to communicate directly with the help of electroencephalogram (EEG) signal, generated by the thought process of the brain. Thought generation inside the brain is a dynamic process, and plenty thoughts occur within a small time window. Thus, there is a need for a BCI device that can distinguish these various ideas simultaneously. In this research work, our previous binary-class Mental Task classification has been extended to the multi-class Mental Task problem. The present work proposed a novel feature construction scheme for multi Mental Task classification. In the proposed method, features are extracted in two phases. In the first step, the wavelet transform is used to decompose EEG signal. In the second phase, each feature component obtained is represented compactly using eight parameters (statistical and uncertainty measures). After that, a set of relevant and non-redundant features is selected using linear regression, a multivariate feature selection approach. Finally, optimal decision tree based support vector machine (ODT-SVM) classifier is used for multi Mental Task classification. The performance of the proposed method is evaluated on the publicly available dataset for 3-class, 4-class, and 5-class Mental Task classification. ExperiMental results are compared with existing methods, and it is observed that the proposed plan provides better classification accuracy in comparison to the existing methods for 3-class, 4-class, and 5-class Mental Task classification. The efficacy of the proposed method encourages that the proposed method may be helpful in developing BCI devices for multi-class classification.
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On the Utility of Power Spectral Techniques With Feature Selection Techniques for Effective Mental Task Classification in Noninvasive BCI
IEEE Transactions on Systems Man and Cybernetics: Systems, 2019Co-Authors: Akshansh Gupta, Jyoti Singh Kirar, Weiping Ding, Chin-teng Lin, Javier Andreu-perez, Ramesh Kumar Agrawal, Mukesh PrasadAbstract:In this paper, classification of Mental Task-root brain-computer interfaces (BCIs) is being investigated. The Mental Tasks are dominant area of investigations in BCI, which utmost interest as these system can be augmented life of people having severe disabilities. The performance of BCI model primarily depends on the construction of features from brain, electroencephalography (EEG), signal, and the size of feature vector, which are obtained through multiple channels. The availability of training samples to features are minimal for Mental Task classification. The feature selection is used to increase the ratio for the Mental Task classification by getting rid of irrelevant and superfluous features. This paper suggests an approach to augment the performance of a learning algorithm for the Mental Task classification on the utility of power spectral density (PSD) using feature selection. This paper also deals a comparative analysis of multivariate and univariate feature selection for Mental Task classification. After applying the above stated method, the findings demonstrate substantial improvements in the performance of learning model for Mental Task classification. Moreover, the efficacy of the proposed approach is endorsed by carrying out a robust ranking algorithm and Friedman's statistical test for finding the best combinations and compare various combinations of PSD and feature selection methods.
Ramaswamy Palaniappan - One of the best experts on this subject based on the ideXlab platform.
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Effects of Hidden Unit Sizes and Autoregressive Features in Mental Task Classification
World Academy of Science Engineering and Technology International Journal of Medical Health Biomedical Bioengineering and Pharmaceutical Engineering, 2007Co-Authors: Ramaswamy Palaniappan, Nai-jen HuanAbstract:Classification of electroencephalogram (EEG) signals extracted during Mental Tasks is a technique that is actively pursued for Brain Computer Interfaces (BCI) designs. In this paper, we compared the classification performances of univariate- autoregressive (AR) and multivariate autoregressive (MAR) models for representing EEG signals that were extracted during different Mental Tasks. Multilayer Perceptron (MLP) neural network (NN) trained by the backpropagation (BP) algorithm was used to classify these features into the different categories representing the Mental Tasks. Classification performances were also compared across different Mental Task combinations and 2 sets of hidden units (HU): 2 to 10 HU in steps of 2 and 20 to 100 HU in steps of 20. Five different Mental Tasks from 4 subjects were used in the experiMental study and combinations of 2 different Mental Tasks were studied for each subject. Three different feature extraction methods with 6 th order were used to extract features from these EEG signals: AR coefficients computed with Burg's algorithm (ARBG), AR coefficients computed with stepwise least square algorithm (ARLS) and MAR coefficients computed with stepwise least square algorithm. The best results were obtained with 20 to 100 HU using ARBG. It is concluded that i) it is important to choose the suitable Mental Tasks for different individuals for a successful BCI design, ii) higher HU are more suitable and iii) ARBG is the most suitable feature extraction method.
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utilizing gamma band to improve Mental Task based brain computer interface design
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2006Co-Authors: Ramaswamy PalaniappanAbstract:A common method for designing brain-computer Interface (BCI) is to use electroencephalogram (EEG) signals extracted during Mental Tasks. In these BCI designs, features from EEG such as power and asymmetry ratios from delta, theta, alpha, and beta bands have been used in classifying different Mental Tasks. In this paper, the performance of the Mental Task based BCI design is improved by using spectral power and asymmetry ratios from gamma (24-37 Hz) band in addition to the lower frequency bands. In the experiMental study, EEG signals extracted during five Mental Tasks from four subjects were used. Elman neural network (ENN) trained by the resilient backpropagation algorithm was used to classify the power and asymmetry ratios from EEG into different combinations of two Mental Tasks. The results indicated that 1) the classification performance and training time of the BCI design were improved through the use of additional gamma band features; 2) classification performances were nearly invariant to the number of ENN hidden units or feature extraction method
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Improving the Performance of Two-state Mental Task Brain-Computer Interface Design Using Linear Discriminant Classifier
EUROCON 2005 - The International Conference on "Computer as a Tool", 2005Co-Authors: Ramaswamy Palaniappan, Nai-jen HuanAbstract:The purpose of this study is to motivate the use of the simpler linear discriminant (LD) classifier as compared to the commonly used multilayer-perceptron-backpropagation (MLP-BP) neural network for brain computer interface (BCI) design. We investigated the performances of MLP-BP and LD classifiers for Mental Task based BCI design. In the experiMental study, EEG signals from five Mental Tasks were recorded from four subjects and the classification performances of different combinations of two Mental Tasks were studied for each subject. Two different AR models were used to compute the features from the electroencephalogram signals: Burg's algorithm (ARB) and least square algorithm (ARLS). The results showed that in most cases, LD classifier gave superior classification performance as compared to MLP-BP, with reduced computational complexity. However, the best Mental Tasks for each subject were the same using both classifiers. ARLS gave the best performance (93.10%) using MLP-BP and (97.00%) using LD. As the best Mental Task combinations varied between subjects, we conclude that for different subjects, proper selection of Mental Tasks and feature extraction methods would be essential for a BCI design
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Brain Computer Interface Design Using Band Powers Extracted During Mental Tasks
Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering 2005., 1Co-Authors: Ramaswamy PalaniappanAbstract:In this paper, a brain computer interface (BCI) is designed using electroencephalogram (EEG) signals where the subjects have to think of only a single Mental Task. The method uses spectral power and power difference in 4 bands: delta and theta, beta, alpha and gamma. This could be used as an alternative to the existing BCI designs that require classification of several Mental Tasks. In addition, an attempt is made to show that different subjects require different Mental Task for minimising the error in BCI output. In the experiMental study, EEG signals were recorded from 4 subjects while they were thinking of 4 different Mental Tasks. Combinations of resting (baseline) state and another Mental Task are studied at a time for each subject. Spectral powers in the 4 bands from 6 channels are computed using the energy of the elliptic FIR filter output. The Mental Tasks are detected by a neural network classifier. The results show that classification accuracy up to 97.5% is possible, provided that the most suitable Mental Task is used. As an application, the proposed method could be used to move a cursor on the screen. If cursor movement is used with a translation scheme like Morse code, the subjects could use the proposed BCI for constructing letters/words. This would be very useful for paralysed individuals to communicate with their external surroundings
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ISSPA - Power and asymmetry ratio of spectral bands for Mental Task recognition
Proceedings of the Sixth International Symposium on Signal Processing and its Applications (Cat.No.01EX467), 1Co-Authors: Ramaswamy Palaniappan, P. RaveendanAbstract:We use the power and asymmetry ratio of spectral bands to recognise Mental Tasks from electroencephalogram signals using a fuzzy ARTMAP neural network. Classical spectral analysis using the Wiener-Khintchine theorem and modem parametric spectral analysis using the autoregressive method are used to obtain these features. The highest classification results of 90% for a subject recognising two Mental Tasks validate the method.